Within image analysis the distance transform has many applications. The distance transform measures the distance of each object point from the nearest boundary. For ease of computation, a commonly used approximate algorithm is the chamfer distance transform. This paper presents an efficient linear-time algorithm for calculating the true Euclidean distance-squared of each point from the nearest ...

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore pre-compute the result of any nearest-neighbor s...

The relationships between the generalized directional derivative of the distance function and the existence of nearest points as well as some geometry properties in Banach spaces are studied. It is proved in the present paper that the condition that for each closed subset G of X and x ∈ X \ G, the Clarke, Michel-Penot, Dini or modified Dini directional derivative of the distance function is 1 o...

ÐSimilarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of highdimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor se...

In [1], Aha et al. introduced a framework and methodology for machine learning, which is instances-based learning. They defined a set of rules for nearest neighbor algorithms and extended them. These algorithms are still in use for knearest neighbor classifier, which is very much related with my topic “similarity search”. In [2], Galper et al. described the similarity search and proposed a dyna...

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor se...

Superconducting phase diagram of the extended Hubbard model supplemented with interaction and hopping terms exceeding nearest neighbour distance in range is analysed systematically at different band-filling and temperature values in a mean-field approximation. The obtained results clearly underline the importance of next-nearest neighbour terms in developing the main superconducting properties ...

Nearest Neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may not be very meaningful for the high dimensional case. Therefore, it is often a complex problem to find...

We introduce a nonparametric model for sensitivity estimation which relies on generating points similar to the prediction point using its k nearest neighbors. Unlike most previous work, the sampled points differ simultaneously in multiple dimensions from the prediction point in a manner dependent on the local density. Our approach is based on an intuitive idea of locality which uses the Voronoi...

The exact computation of the nearest-neighbor spacing distribution P (s) is performed for a rectangular billiard with point-like scat-terer inside for periodic and Dirichlet boundary conditions and it is demonstrated that when s → ∞ this function decreases exponentially. Together with the results of Ref. [13] it proves that spectral statistics of such systems is of intermediate type characteriz...